






"""simple docstring"""


import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		__SCREAMING_SNAKE_CASE   =   False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor




class 						__UpperCamelCase     (     unittest.TestCase						):
					def __init__(							self							:	List[Any]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Dict=7     , UpperCAmelCase							:	str=3     , UpperCAmelCase							:	Any=18     , UpperCAmelCase							:	List[str]=30     , UpperCAmelCase							:	Optional[Any]=400     , UpperCAmelCase							:	List[Any]=None     , UpperCAmelCase							:	str=True     , UpperCAmelCase							:	List[str]=True     , UpperCAmelCase							:	Dict=None     , )	->       Optional[int]:
											lowerCAmelCase   :Any						=			size if size is not None else {'height': 20, 'width': 20}
											lowerCAmelCase   :Any						=			parent
											lowerCAmelCase   :Any						=			batch_size
											lowerCAmelCase   :Tuple						=			num_channels
											lowerCAmelCase   :Optional[int]						=			image_size
											lowerCAmelCase   :Any						=			min_resolution
											lowerCAmelCase   :str						=			max_resolution
											lowerCAmelCase   :Optional[int]						=			size
											lowerCAmelCase   :Optional[Any]						=			do_normalize
											lowerCAmelCase   :Tuple						=			do_convert_rgb
											lowerCAmelCase   :Tuple						=			[512, 1024, 2048, 4096]
											lowerCAmelCase   :List[str]						=			patch_size if patch_size is not None else {'height': 16, 'width': 16}
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       int:
											return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
					def        UpperCAmelCase__ (							self							:	Any   )	->       int:
											lowerCAmelCase   :Any						=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
											lowerCAmelCase   :List[str]						=			Image.open(requests.get(UpperCAmelCase     , stream=UpperCAmelCase   ).raw   ).convert('RGB'   )
											return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""   ,     )
@require_torch
@require_vision
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Any      =  PixaStructImageProcessor if is_vision_available() else None
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :Optional[Any]						=			PixaStructImageProcessingTester(self   )
					@property
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Optional[int]:
											return self.image_processor_tester.prepare_image_processor_dict()
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       List[Any]:
											lowerCAmelCase   :List[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_normalize'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_convert_rgb'   )   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :Tuple						=			self.image_processor_tester.prepare_dummy_image()

											lowerCAmelCase   :Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict   )
											lowerCAmelCase   :Optional[int]						=			2048

											lowerCAmelCase   :int						=			image_processor(UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase   )
											self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0_6_0_6   )     , atol=1e-3     , rtol=1e-3   )   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       List[Any]:
											# Initialize image_processor
											lowerCAmelCase   :List[str]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											lowerCAmelCase   :int						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , Image.Image   )

											# Test not batched input
											lowerCAmelCase   :str						=			(
											    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	lowerCAmelCase   :int						=			image_processor(
																	    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

																	# Test batched
																	lowerCAmelCase   :Optional[int]						=			image_processor(
																	    UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )
					def        UpperCAmelCase__ (							self							:	int   )	->       Union[str, Any]:
											# Initialize image_processor
											lowerCAmelCase   :Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											lowerCAmelCase   :str						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , Image.Image   )

											# Test not batched input
											lowerCAmelCase   :List[str]						=			(
											    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
											    * self.image_processor_tester.num_channels
											) + 2

											lowerCAmelCase   :int						=			True

											for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	with self.assertRaises(UpperCAmelCase   ):
																							lowerCAmelCase   :int						=			image_processor(
																							    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches

																	lowerCAmelCase   :List[str]						=			'Hello'

																	lowerCAmelCase   :Any						=			image_processor(
																	    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase     , header_text=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

																	# Test batched
																	lowerCAmelCase   :Union[str, Any]						=			image_processor(
																	    UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase     , header_text=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Any:
											# Initialize image_processor
											lowerCAmelCase   :List[str]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											lowerCAmelCase   :Optional[Any]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , numpify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , np.ndarray   )

											lowerCAmelCase   :Dict						=			(
											    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	lowerCAmelCase   :str						=			image_processor(
																	    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

																	# Test batched
																	lowerCAmelCase   :Union[str, Any]						=			image_processor(
																	    UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Tuple:
											# Initialize image_processor
											lowerCAmelCase   :List[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											lowerCAmelCase   :Any						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , torchify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , torch.Tensor   )

											# Test not batched input
											lowerCAmelCase   :str						=			(
											    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	lowerCAmelCase   :Tuple						=			image_processor(
																	    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

																	# Test batched
																	lowerCAmelCase   :Dict						=			image_processor(
																	    UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""   ,     )
@require_torch
@require_vision
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				int      =  PixaStructImageProcessor if is_vision_available() else None
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Tuple:
											lowerCAmelCase   :Dict						=			PixaStructImageProcessingTester(self     , num_channels=4   )
											lowerCAmelCase   :int						=			3
					@property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Union[str, Any]:
											return self.image_processor_tester.prepare_image_processor_dict()
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Optional[int]:
											lowerCAmelCase   :List[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_normalize'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_convert_rgb'   )   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Union[str, Any]:
											# Initialize image_processor
											lowerCAmelCase   :List[str]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											lowerCAmelCase   :List[str]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , Image.Image   )

											# Test not batched input
											lowerCAmelCase   :Optional[Any]						=			(
											    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
											    * (self.image_processor_tester.num_channels - 1)
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	lowerCAmelCase   :List[Any]						=			image_processor(
																	    image_inputs[0]     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

																	# Test batched
																	lowerCAmelCase   :Optional[int]						=			image_processor(
																	    UpperCAmelCase     , return_tensors='pt'     , max_patches=UpperCAmelCase   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )







"""simple docstring"""


from math import loga


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if a < 0:
												raise ValueError('Input value must be a positive integer' )
						elif isinstance(a__     ,      a__ ):
												raise TypeError('Input value must be a \'int\' type' )
						return 0 if (a == 0) else int(loga(a & -a ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()